7 citations · 33 across the 12 of their papers we have counts for
6 papers · 1 filter
Njord: A Probabilistic Graph Neural Network for Ensemble Ocean Forecasting
Daniel Holmberg, Joel Oskarsson, Erik Wikingsson +2
Ocean dynamics are inherently chaotic, yet existing machine learning ocean models produce only deterministic forecasts. We introduce Njord, a probabilistic data-driven model for oc…
CRPS-LAM: Probabilistic Regional Weather Forecasting with Continuous Ranked Probability Score
Erik Larsson, Joel Oskarsson, Tomas Landelius +1
Limited-Area Models (LAMs) enable weather forecasting over regional domains at higher resolutions than what is computationally feasible for global models. At such high resolutions,…
Diffusion-LAM: Probabilistic Limited Area Weather Forecasting with Diffusion
Erik Larsson, Joel Oskarsson, Tomas Landelius +1
Machine learning methods have been shown to be effective for weather forecasting, based on the speed and accuracy compared to traditional numerical models. While early efforts prim…
Continuous Ensemble Weather Forecasting with Diffusion models
Martin Andrae, Tomas Landelius, Joel Oskarsson +1
Weather forecasting has seen a shift in methods from numerical simulations to data-driven systems. While initial research in the area focused on deterministic forecasting, recent w…
Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks
Joel Oskarsson, Tomas Landelius, Marc Peter Deisenroth +1
In recent years, machine learning has established itself as a powerful tool for high-resolution weather forecasting. While most current machine learning models focus on determinist…
Graph-based Neural Weather Prediction for Limited Area Modeling
Joel Oskarsson, Tomas Landelius, Fredrik Lindsten
The rise of accurate machine learning methods for weather forecasting is creating radical new possibilities for modeling the atmosphere. In the time of climate change, having acces…